Multi-label learning has many applications in text categorization [1]}, bioinformatics [2]}, web mining [3]}, etc. In recent years, multi-label learning has also been used in semantic decoding from brain activity [4]}, [5]}, [6]}. However, the study of multi-label emotion decoding is still lacking. Besides, in multi-view learning, the information in some views are useful to handle the weakness of other views. Furthermore, multi-view learning can naturally be embedded into multi-label learning tasks to improve the classification performance. Models in multi-view multi-label learning can be divided into generative models and discriminative models. [7]} proposed a Gaussian mixture VAE placing a conditional Bernoulli mixtures distribution on the labels \(\textbf {y}\) , which belongs to a probabilistic generative model. [8]} used matrix factorization to uncover the latent patterns among different views for more accurate multi-label classification. [9]} exploited shared subspace for fusing multi-view representations and also took view-specific information into consideration. The above two models are both attributed to the discriminative model. Besides, [10]} proposed deep hybrid models which can bridge the discriminative and generative models in which the former often attain higher predictive accuracy, while the latter are more strongly regularized and can obtain more expressive representations. The discriminative component of the hybrid model pays more attention to the predict probability \(p(\textbf {y}|\textbf {x})\)  rather than the distribution of the label \(p(\textbf {y})\) . Inspired by this, we propose a multi-view multi-label hybrid model for learning the expressive neural representations and predicting multiple emotional states accurately.
